In this study the effects of school and classroom climate and teacher's behavior on Italian students' mathematical achievement score in PISA 2012 were investigated. Simple and scale indices provided by the PISA database, constructed by responses from the students' and principals' background questionnaires, were considered as predictive variables of the math achievement scores. Multilevel models including all the predictive variables, controlling for some relevant student, family background and school variables, confirmed that perceptions of school and classroom climate and teachers' behavior influence mathematics performance in PISA. In particular, the effect of the teacher's use of cognitive activation strategies had the strongest positive effect, followed by the school and classroom climate indicators. Thus, more cognitively activating instruction and an orderly and peaceful atmosphere in schools and classrooms encourage students and help to transform existing interests into mathematic achievement. Our

The popularity of the cluster analysis in the tourism field has massively grown in the last decades. However, accordingly to our review, researchers are often not aware of the characteristics and limitations of the clustering algorithms adopted. An important gap in the literature emerged from our review regards the adoption of an adequate clustering algorithm for mixed data. The main purpose of this article is to overcome this gap describing, both theoretically and empirically, a suitable clustering algorithm for mixed data. Furthermore, this article contributes to the literature presenting a method to include the “Don’t know” answers in the cluster analysis. Concluding, the main issues related to cluster analysis are highlighted offering some suggestions and recommendations for future analysis.

The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information. Properly, an Exponential distance-based Fuzzy Partitioning Around Medoids algorithm with spatial penalty term has been proposed to classify the spline representation of the time trajectories. The results show that the heterogeneity among regions along with the spatial contiguity is essential to understand the spread of the pandemic and to design effective policies to mitigate the effects.

The spatial epidemic dynamics of Covid-19 outbreak in Italy were modelled by means of an Object-Oriented Bayesian Network in order to explore the dependence relationships, in a static and a dynamic way, among the weekly incidence rate, the intensive care units occupancy rate and that of deaths. Following an autoregressive approach, both spatial and time components have been embedded in the model by means of spatial and time lagged variables. The model could be a valid instrument to support or validate policy makers’ decisions strategies.

The detection of spatially contiguous clusters is a relevant task in geostatistics since near located observations might have similar features than distant ones. Spatially compact groups can also improve clustering results interpretation according to the different detected subregions. In this paper, we propose a robust metric approach to neutralize the effect of possible outliers, i.e. an exponential transformation of a dissimilarity measure between each pair of locations based on non-parametric kernel estimator of the direct and cross variograms (Fouedjio, 2016) and on a different bandwidth identification, suitable for agglomerative hierarchical clustering techniques applied to data indexed by geographical coordinates. Simulation results are very promising showing very good performances of our proposed metric with respect to the baseline ones. Finally, the new clustering approach is applied to two real-word data sets, both giving locations and top soil heavy metal concentrations.